This repo contains player advanced stats and Elo ratings for WNBA history.
The file wnba-player-stats.csv
contains season-level advanced stats for WNBA players by team for the 1997-2019 seasons, from Basketball-Reference.com. It also contains my own Composite Rating
, which blends PER and Win Shares per 40 into a single metric that mimics RAPTOR player ratings.
Category | Description |
---|---|
player_ID | BB-Ref player ID |
Player | Player name |
year_ID | Season |
Age | Age (as of Jul. 1) |
Tm | Team played for |
tm_gms | Team's scheduled games |
Tm_Net_Rtg | Team's net efficiency (offensive rating minus defensive rating) |
Pos | Player's position played |
G | Games played |
MP | Minutes played |
MP_pct | Percentage of available minutes played |
PER | Player Efficiency Rating |
TS_pct | True Shooting Percentage |
ThrPAr | Three-point Attempt Rate (3PA/FGA) |
FTr | Free Throw Rate (FTA/FGA) |
ORB_pct | Offensive rebound percentage |
TRB_pct | Total rebound percentage |
AST_pct | Assist percentage |
STL_pct | Steal percentage |
BLK_pct | Block percentage |
TOV_pct | Turnover percentage |
USG_pct | Usage percentage |
OWS | Offensive Win Shares |
DWS | Defensive Win Shares |
WS | Total Win Shares |
WS40 | Win Shares per 40 minutes |
Composite_Rating | Estimated net points added per 100 possessions |
Wins_Generated | Wins implied by Composite Rating |
Composite Rating is determined by the following formula (based on NBA player stats):
Rating = -5.237248 + 0.1741241*PER + 26.0059929*WS40
Individual ratings are then adjusted so the team's weighted average Composite Rating (times 4.064, a scalar to account for score effects) equals the team's Net Rating. Wins Generated are derived by divvying up the team's Net Rating-implied wins according to each player's contribution to the team's Net Rating.
The file wnba-team-elo-ratings.csv
contains Elo Ratings for every team in WNBA history on a game-by-game basis. The ratings were developed by FiveThirtyEight's Jay Boice, similar to the basic ratings for the NBA. The ratings change after every game based on the winner's pregame win probability, with more unexpected wins resulting in more points shifting from the loser's rating to the winner's.
Category | Decription |
---|---|
season | Year of game |
date | Date of game |
team1 | First team listed's ID |
team2 | Second team listed's ID |
name1 | Team1's full name |
name2 | Team2's full name |
neutral | Was game at a neutral site? (1=yes) |
playoff | Was game in playoffs? (1=yes) |
score1 | Team1's points in game |
score2 | Team2's points in game |
elo1_pre | Team1's pregame Elo rating |
elo2_pre | Team2's pregame Elo rating |
elo1_post | Team1's postgame Elo rating |
elo2_post | Team2's postgame Elo rating |
prob1 | Team1's pregame odds of winning |
is_home1 | Was Team1 the home team? (1=yes) |
Some other pertinent information about WNBA Elo ratings:
- Home court advantage is 80 (NBA=100)
- K-factor is 32 (NBA=20)
- Teams are reverted by 1/2 between seasons (NBA=1/4)
- In playoffs, elo difference is multiplied by 1.25
- Margin of victory -- relative to expectation -- matters, same as NBA
- Expansion teams start at 1300
There were five teams that moved to different cities over the years; in those cases, ratings were carried over from the previous team:
- 2003: ORL -> CON
- 2003: UTA -> SAS
- 2010: DET -> TUL
- 2016: TUL -> DAL
- 2018: SAS -> LVA